Method of assisting a sales representative in selling

ABSTRACT

A computer implemented method of providing feedback to a sales representative for a sales opportunity to a customer, comprising: determining, in dependence on information received from the sales representative, a probability that the sales opportunity will result in a sale by the sales representative; determining, in dependence on information received from the sales representative, a location on a sales cycle time line for the sales opportunity; assigning a priority to the sales opportunity in dependence on the location on the sales cycle time line and the probability that the sales opportunity will result in a sale; and rendering on a display a visual representation of the probability and the priority.

This application is a divisional of and claims the benefit of U.S.patent application Ser. No. 11/790,928, filed Apr. 30, 2007, which is adivisional of and claims the benefit of U.S. patent application Ser. No.09/983,180, filed Oct. 23, 2001 and issued May 8, 2007 as U.S. Pat. No.7,216,087, which is incorporated by reference, which claims the benefitof U.S. Provisional Application Ser. No. 60/242,438, filed Oct. 24,2000, all of which are hereby incorporated herein in their entirety byreference.

BACKGROUND OF THE INVENTION

This invention relates to a method of assisting a sales representativein selling and a program storage device readable by a processor of acomputer and a computerized sales automation system for use inimplementing same.

Sales professionals are continually faced with the task of managinglarge amounts of information concerning details on customers,organizations, and situations in which they are actively selling theirproducts and services. As computers have become much more powerful andmore economical for companies to purchase, sales departments haveequipped their sales representatives with computers. The use ofcomputers to assist in sales is usually referred to as sales automation.The term “sales automation” has been used to cover a wide range ofcomputer sales applications, from computer-based multimediapresentations to “Rolodex” type storage of customer information. Priorsales automation systems, however have shortcomings in helping the salesrepresentative. For example, many are incapable of processinginformation regarding actual events or individual assessments of thesales situation to suggest winning strategies to change activities and,interactions, so as to improve the chances of winning the sale.

Applying computer technology to the sales force presents two mainchallenges: the first is choosing the correct computer environment torun the application software, and the second is to choose applicationsoftware that is the most effective in assisting the salesrepresentative to win more sales. A sales force is usually mobile, soportable computers (laptops) are the most convenient. Laptop computersshould be equipped with modems in order for salespeople to communicatewith head office and other remote team members. The entire sales teamincluding managers, representatives, and support staff, should beconnected or networked. Client-server systems, where a main databaseresides on a central server and the clients access the information inthe database via the network, are the most widely chosen platforms. Somesystems now use the Internet as the network of choice for communication.

Recently, a new type of software, called groupware, has becomeavailable. Groupware is specifically designed to allow a distributedgroup of people to work together effectively as a team. It has its owne-mail and can also facilitate group discussions electronically. Theseelectronic communication functions are commonly referred to as“messaging”. In addition to messaging, another necessary component ofgroupware is to be able to design applications using the messaging as anunderpinning, or platform, for distributed teams of people separated bytime and geography, to accomplish a common project or mission. Thetypical sales team, consisting of manager, in-house administration, andmobile or roving sales representatives, represent the ideal model forthe technology tool of groupware.

One category of software called “contact management software” isdesigned to allow sales representative to store information about thecustomer. This information usually centers on details of the customer'sorganization, professional life, interactions with the salesrepresentative, and is stored in an ‘ad hoc’ manner. Sales opportunitiesare the actual situations in which sales representatives are activelyselling to customers. With this type of software it is difficult tologically store information about the sales opportunity and theassociated sales cycle, and therefore it has limited functionality inassisting the sales representative in the selling process. Contactmanagement software is also limited in its networking capability, makingit difficult for a distributed team to share and collaborateeffectively.

Another kind of software called “Sales Automation Software” covers awider range of functionality and usually provides the capability tostore information on sales opportunities. At a given point, a goodrepresentative may be working on dozens of sales situations, all atvarious stages of progress. Sales automation software allows thesesituations to be reported, characterized by percentage chances ofsuccess, and prioritized in a number of ways to aid a manager inforecasting, or to aid the sales representative in determining whichsituation to work on next.

A weakness with most sales automation programs is that they concentratemore on organizing lists of information, rather than defining theprocess that occurs within the opportunity. They are therefore unable touse the computer's power to proactively assist the sales representativethrough the period of time in which he or she is actively trying to sellto the customer.

Another major weakness of current sales automation programs is theinadequate way they calculate two important parameters essential toobtaining the full value of the software—probability and priority.Prioritizing a portfolio of fifty to one hundred sales opportunities atdifferent stages of the sales cycle is a challenge for any salesrepresentative. To do it effectively you need not only a computer, but away to accurately assess the probability and priority that should beassigned to the sales opportunity.

Probability is typically a numerical value in the form of a percentagedescribing the chances that the sale will be won by the salesrepresentative. One way to get this number is to simply ask thesalesperson to enter his gut feel on winning the sale, in terms of apercentage between one and one hundred. This method is notoriouslyinconsistent between different sales representatives, even with the samerepresentative forecasting different sales situations. As probability isused by the sales manager to forecast future business, the moreinaccurate the forecasting method, the more potential harm to thecompany.

Another method to evaluate probability is to divide the sales cycle intoa number of well defined stages, and to “award” the salesperson acertain percentage chance of winning the sale based on which stage theyare at in the cycle. This method does not take into account the factthat no one knows half way through a six month sales cycle whether thesale will come to fruition—sometimes budgets get frozen, or needschange. The performance of the salesperson compared with the competitionis also ignored in this method. There is no distinction between a goodor a bad salesperson at the “demonstration” part of the sales cycle—theyare each awarded a sixty percent chance of success, solely because theyhave reached this part of the sales cycle. This method has the potentialto be more inaccurate than the simple process of asking for “gut feel”percentages.

Current methods of prioritizing sales opportunities are also inadequate.Usually the salesperson's current sales situations are listed in thesales automation program in order of probability, with the most probableat the top. This is especially dangerous if the method of determiningprobability is based on which sales step the salesperson has reached inthe sales cycle. This method allocates the highest percentages to thelate stages of the sales cycle—therefore the priority list sorted withthe high probabilities at the top will drive the salesperson to workonly on those situations at the point of closing or finishing. In fact,a salesperson must allocate his work evenly throughout the salescycle—he cannot expect to win sales that he has neglected in the earlystages of development. Current sales automation programs do not go farenough in assisting the sales representative in the sales cycle—thearena where sales skills are used competitively to fight for the sale.

Some sales automation programs do recognize that selling can bedescribed as a process, involving a sequence of well defined steps, andrequest that the sales representative enter or check off when he movesfrom one step to another (they are reactive rather than proactive).These programs tend to involve simple rules, such as, “two weeks aftersending a quotation, follow-up with a phone call”. Simple rules can helpremind a sales representative, but in fact, the sales cycle is quitecomplicated and difficult to represent adequately through the sameserial sequence of steps. For example, a typical sales cycle involves alarge number of interactions between the sales representative andcustomer, such as giving quotations and demonstrations. During theseinteractions, the sales representative is applying his knowledge of thefundamental skills of selling. At the same time, as the sale progresses,the salesperson is developing knowledge from the information gained, andassessing his current performance in order to plan new strategies orchange current ones. Also, because the selling process is highlydependent on human behavioral patterns, no two selling situations areidentical.

Thus there is a need to provide an improved method and system ofassisting the sales representative in their selling efforts, including aneed for a sales automation system that can provide meaningful guidanceand coaching to the sales representative during the sales process. Thereis also a need for a model that describes the sales process in a waythat it can be stored in a computer system for providing feedback to theuser. Further, there is a need for an improved method to grade salesopportunities according to the probability that the sale will be won bythe sales representative. Ideally, such a method should use a minimum ofdata input and so as to provide consistency across a large sales teamand amongst multiple sales opportunities belonging to the samesalesperson. It is also desirable to provide a method of prioritizing alist of sales opportunities where a salesperson has the confidence towork from the top to the bottom of the list, knowing that he isoptimally spreading his efforts to effectively cover the sales cyclesfrom start to finish.

SUMMARY OF THE INVENTION

According to one aspect of the present invention there is provided acomputer implemented method of assisting a sales representative withprioritizing a plurality of sales opportunities. The method includessteps of: for each of the sales opportunities, determining a probabilityfrom a finite number of possible probability values that the salesrepresentative will ultimately win the sale; establishing a sales cycletime line for each of the sales opportunities and dividing each of therespective sales cycle time lines into a uniform number and type ofselling phases; for each sales opportunity, assigning a time-dependentpriority value based on the determined probability values for the salesopportunity and the selling phase that the sales opportunity is in atthe time that the priority value is assigned; and providing on a visualdisplay for the sale representative an indication of the time-dependentpriority value assigned to at least one of the sales opportunities.

According to another aspect of the invention, there is provided acomputer implemented method of providing feedback to a salesrepresentative for a sales opportunity to a customer that includes:determining, in dependence on information received from the salesrepresentative, a probability that the sales opportunity will result ina sale by the sales representative; determining, in dependence oninformation received from the sales representative, a location on asales cycle time line for the sales opportunity; assigning a priority tothe sales opportunity in dependence on the location on the sales cycletime line and the probability that the sales opportunity will result ina sale; and rendering on a display a visual representation of theprobability and the priority.

According to still a further aspect of the inventions, there is provideda computerized sales advisor system for advising a sales representativeabout a sales opportunity to a customer, the system comprising: an inputdevice for receiving information relating to an actual salesopportunity; an information storage device for storing information,including inputted information; a processor for reading and processinginput from the input device and information from the storage device inaccordance with a program of instructions to produce an output response;and a visual output device for presenting the output response to thesales representative. The processor is configured for: determining, independence on information received from the sales representative, aprobability that the sales opportunity will result in a sale by thesales representative; determining, in dependence on information receivedfrom the sales representative, a current location on a sales cycle timeline for the sales opportunity; assigning a priority to the salesopportunity in dependence on the current location on the sales cycletime line and the probability that the sales opportunity will result ina sale; and rendering on a display of the output device a visualrepresentation of the probability and the priority.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood with reference to the followingdescription of the preferred embodiment and drawings in which:

FIG. 1 is a conceptual diagram of various components of a salesautomation system according to embodiments of the present invention;

FIG. 2 is a block diagram of a networked client-server computer systemon which the present invention can be implemented;

FIG. 3 a is a simple diagram comparing actual, customer's and idealsales cycles;

FIG. 3 b is a graphical representation of a “degree of focus” oremphasis placed on the fundamental sales skills of probing, proving, andclosing as a function of time during a sales cycle;

FIGS. 4 a-c are graphical representations similar to that of FIG. 3 bfurther showing how the relative lengths of the phases in which theprobing, proving, and closing skills respectively predominate can bedifferent for different sales situations;

FIG. 5 is a graphical representation similar to that of FIG. 1 b furtherillustrating examples of critical interactions (i.e. activities) thatcan occur through a typical sales cycle and the time of suchinteractions within the sales cycle;

FIG. 6 a is a representation of a 3×3 probability matrix;

FIG. 6 b is a chart summarizing the information contained in theprobability matrix of FIG. 6 a;

FIG. 6 c is an alternative representation of the probability matrix ofFIG. 6 a in which probability percentages are shown in place of theprobability indices;

FIG. 7 is an exemplary horizontal bar graph representing the salescycles associated with twenty sales opportunities occurring over thecourse of one year;

FIG. 8 a-b are diagrams showing how different suggestions for action anddifferent priority rankings can be generated depending on the phase ofthe sales cycle;

FIG. 9 is a chart summarizing exemplary messages and priority rankingswhich can be assigned to each probability index value in each of theprobe, prove and close phases;

FIG. 10 is a representation of a priority cube;

FIG. 11 is a representation of a user interface used to set up a salesmodel;

FIG. 12 illustrates a data entry screen which is used by a salesrepresentative to determine the beginning and end of a sales cycle;

FIG. 13 are graphical representations similar to that of FIG. 3 bfurther showing how the critical interactions can be scaled to fit thesales cycle length which can be changed by a user at any time in lightof new information;

FIG. 14 illustrates a further data entry screen used by a salesrepresentative to record details of a customer interaction;

FIG. 15 is a graphical representation showing customer interaction data;

FIG. 16 illustrates a display screen used to display data and alsomodify data concerning a sales opportunity;

FIGS. 17 a-17 c show additional data entry screens used to enterinformation during each phase of the sales cycle (FIG. 17 a—probe; FIG.17 b—prove; FIG. 17 c—close);

FIG. 18 shows a graphical user interface for an intelligent responsemode of operation;

FIGS. 19 a-19 b show a table of possible advisor messages providedduring the intelligent response mode of operation;

FIG. 20 a-20 i show diagrammatic representation of additional possibleadvisor messages provided during the intelligent response mode ofoperation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present method is especially suited to be implemented by softwareloaded on a computer to produce a computerized sales automation system.Preferably, all members of a sales team, from the field salesrepresentative, his or her support colleagues and sales management willhave a computer networked to a system that allows information sharing.

A simplified block diagram illustrating conceptual components of a salesautomation system 10 according to preferred embodiments of the inventionis shown in FIG. 1. The system 10 includes a user interface 130 thatallows a user (typically a sales representative) to enter informationinto and receive information from the system.

The system further includes a sales model module 100, a rules processor140, an interaction monitor 160, and an information monitor 150. Thesales model module is configured to provide and store, with input from asales manager, a sales model of the typical sales cycle for the productor service being marketed, and also to track, with input from the salesrep, the actual sales cycle as it happens. The interaction monitor 160takes information from the user, through the user interface 130, on typeand number of critical interactions which occur as the sales cycleprogresses, and compares this information with what should havehappened, as defined in the sales model. The information monitor 150acts in a similar manner as the interaction monitor, in that it acceptsinformation from the sales representative and compares the amount andquality of information input by the sales representative with the salesmodel. The results of the comparisons that are performed by theinteraction monitor 160 and the information monitor 150 are passed tothe rules processor, 140 which determines a response according to theinformation received. In a preferred embodiment, the rules processorapplies a preprogrammed set of criteria to determine the nature and typeof an intelligent response with the objective of providing the user (ifnecessary) with suggestions on how they should adapt their sellingstrategies to minimize any gap between real life performance, andperformance as represented by the sales model.

The sales model module 100 provides a model that describes the typicalsales cycle. The model encompasses all aspects of the sales cycleincluding events and interactions that occur within in it. The model isconstructed such that it may be easily stored in a computer.

According to the present invention, during the sales process, the salesrepresentative enters information into the computer. This process ofdata entry is preferably designed to reduce administrative overhead tothe user; there is a minimum requirement for typing and most informationcan be gathered through the use of preprogrammed pick-lists. Theinformation on what is really happening in the sale is compared by thecomputer to model information relating to the model sales opportunity,as stored in the computer. The difference, or “gap”, between the two isprocessed through a set of rules to derive an intelligent response tothe user. The response is derived to modify the user's selling strategyin order to minimize the gap as future activity information is enteredinto the computer.

The present invention produces most benefits as a tool for the salesteam when it is implemented in a networked computer system 180 (see FIG.2). Client-server computer architectures are ideally suited to this typeof application. The embodiment described here uses the groupwareproduct, Lotus Notes, although other technologies that encouragecollaborative networked work processes could also be used. A maindatabase 186 containing all critical interactions between sales peopleand customers is stored on a central server computer 184. All users canaccess portions of the database pertinent to their specificresponsibilities. The information can either be used directly over thenetwork 188, or can be downloaded to a remote computer 182 through aprocess called “replication”. After replicating, the remote computer 182can be disconnected from the network, and the user can add or edit data.Changes will later be synchronized with the main database, during thenext replication. If two parties have made changes to the same piece ofinformation, replication conflicts are marked. The net effect of thereplication process is to maintain a synchronized central database ofinformation, gathered by a team who may be distributed in geography andtime.

In a preferred embodiment of the invention, intelligent agents are usedto process large amounts of information according to a prescribed set ofrules. These agents are small pieces of computer code, or macros, thatoperate on the server computer 184 at predetermined intervals and duringperiods when there is inactivity on the network (i.e. at night). Theyare necessary to perform the essential requisite of the describedmethodology; that is, to compare actual events and activities against amodel describing what should be happening.

An overview of the invention having been provided above, the variousconceptual components and their interaction will now be described ingreater detail.

The Sales Model

The sales model provided by module 100 describes what happens in a salescycle in a way that can be successfully modeled in a computer basedsales automation system. A sales opportunity is a real situation wherethe sales representative has identified that he has a strong possibilityof selling his product. The customer's sales cycle is defined herein tomean the time period from the point that a customer initiates the buyingprocess to the point when he makes his final decision to buy from aselected vendor. The sales opportunity always has an associatedcustomer's sales cycle and is therefore the time over which the salesopportunity takes to mature and come to completion. The customer's salescycle is also the time during which the sales representative has thechance to show how successful he is at using his selling skills.

During the customer's sales cycle numerous interactions occur betweenthe sales representative and the customer as part of the sellingprocess. A large component of the selling process is dependent on humaninteractions, which is difficult to model in the computer. Thiscomponent can be referred to as “the art of selling”. There is anothercomponent of selling, however, that is dependent on well-establishedtechniques and rules that have been developed and proven over manyyears, which can be referred to as “the science of selling”. The salesmodel used in the present invention models the selling process usingprinciples more properly characterized as belonging to the science ofselling.

The sales model is a “picture” of all the important interactions andevents that occur throughout the sales cycle, and which describe thesales process. The model is constructed using four key aspects whichdescribe the sales cycle and processes occurring within it. These fouraspects are:

1. Time

The essence of the sales cycle model resides in the exact description ofthe sales cycle itself—the sales cycle has a beginning and an end, andtherefore has a certain duration of time. The beginning of the salescycle is defined as the point of time when the customer starts thebuying process. While this point cannot be defined exactly, certaincustomer actions are usually good indicators that the process iscommencing. Such actions include, for instance, applying for a budgetfor the product or service, or gathering information about the availableoptions. Salespeople should always be vigilant about identifying thepoint that the customer starts the buying process—this gives them moretime to sell, and therefore a better chance to win. This inventionreinforces this philosophy and therefore contributes to higher sales.

The end of the sales cycle comes when the customer makes a finaldecision to give his business to a selected vendor. This point is easierto ascertain than the beginning of the sales cycle. The challengethough, is for the salesperson to forecast this date—in other words toforecast the length of the sales cycle. The sales rep must estimate thesales cycle end date and enter it into the computer at the start of thesafe, and update the estimate throughout the sales cycle as the dynamicsof the sale change. Again, indicators from the customer are needed toassess the end date. The customer can be asked directly, “when would youlike the product delivered?”. If the answer is unclear or imprecise thesalesperson should make his best estimate and refine it as the saleprogresses.

For a given product or service there is defined an ideal sales cycle.This can be established by reviewing data on a large number of pastsales and using past experience to determine how long it should take towin the sale in the ideal world. Ideal sales cycles vary considerablyaccording to product and industry sector. There will also inevitably be“actual” sales cycles that are shorter or longer than the ideal for agiven product. An example of a short actual sales cycle is a situationwhere the sales representative has discovered the sales opportunitylate, that is, after the customer has commenced the buying process. Anexample of a long actual sales cycle is a situation where the salesrepresentative has discovered the sales opportunity very early on in thecustomer's buying process. FIG. 3 a shows examples of the customer'ssales cycle, a short actual sales cycle and the ideal sales cycle.

In a preferred embodiment, the sales model is applied to the actualsales cycle, which is defined herein to mean the sales cycle measuredfrom the point of time that the sales representative first learns thatthe customer has started the buying process. If the opportunity isdiscovered very late in the cycle, then the sales representative mustobviously make up for lost time. All aspects of the model are scaled intime according to the actual sales cycle, even though the model has beenset up for the ideal sales cycle.

Time is an important parameter of the model, as each event, interaction,and piece of information is identified relative to when it happened inthe sales cycle, and compared to when it should have happened, if it istime sensitive information.

2. Sales Skills

The sales model defines three fundamental sales skills—probing, proving,and closing. The sales cycle is divided into three consecutive timephases named probe, prove, and close, after the sales skill mostdominant in that phase. These sales skills are described as follows:

Probing: the process where the sales representative finds out thedetails of the customer's requirements and needs. Also, the wholebackground of the sale is established, for example—“Who are thedecision-makers?”, “Who are the competitors?”, “Does the customer havesufficient funds to buy the product?”. In the sales model of the presentinvention, probing is the dominant skill used in a first phase—the probephase.

Proving: the process whereby a sales representative demonstrates to thecustomer how the features of his product or service can satisfy theirrequirements. This process usually involves a demonstration of theproduct itself. Proving, is dominant during phase two of the salescycle—the prove phase.

Closing: the process of establishing if the customer has enoughinformation to make a purchase and if there are any reasons why thecustomer feels they should not buy the product or service. Customerobjections must be drawn out and addressed by the sales representative.The goal in closing is to obtain a customer commitment, once all issueshave been addressed successfully. Closing can only proceed when thecustomer has learned enough about the sales representative's product orservice in order to make an intelligent decision. Closing is thedominant skill in phase three of the sales cycle—the close phase.

FIG. 3 b shows a diagrammatic representation of the three-phase salescycle according to the present inventive model. The sales cycle isrepresented by the horizontal axis, 101, in units of time. The verticalaxis, 102, represents the degree to which the sales representative isusing the sales skill of probing, proving or closing. This axis islabeled “Degree of Focus” and has values from zero to one hundredpercent. The model shows how the use of probing varies as a function oftime by the probe curve 103. Similarly, with the proving process, curve104 shows how this skill is used over time throughout the sales cycle.Curve 105 shows how the use of the closing skill is used in the salescycle.

According to the present model, at any given point in the sales cycle,the three degrees of focus on proving, probing, and closing must sum to100%. For example, at point A in FIG. 13, 106, the degree of focus onprobing and proving is 95% and 5% respectively. At point B, 107, thedegree of focus on probing is 50% and that of proving, is also 50%. Atpoint C, 108, the degree of focus on probing, proving and closing is 5%,5%, and 90% respectively. Although the model in FIG. 3 b is shown asquantitative, this is not crucial to the success of its implementation.What is important is the overall shape of each probe, prove, and closecurve, and their rough relative values at a particular point in timewithin the sales cycle.

The probe, prove, and close curves represent how much each of theseskills should be used throughout the sales process within the salescycle. The model provides that in the early stages, probing is thedominant process, in the middle stages proving is the dominant process,and in the later stages closing is the dominant process. An importantfeature of the present model is that it allows for the three fundamentalskills to occur within the same interaction. The phase of the salescycle in which a particular skill is dominant is labeled with the nameof that skill. Therefore, the probe skill is dominant through the probephase, which is represented by 109 in FIG. 3 b. The prove skill isdominant through the prove phase, which is represented by 110 in FIG. 3b. Closing is the predominant skill in the close phase, which isrepresented by 111 in FIG. 3 b.

The transition point between the probe and the prove phase is the pointat which the prove curve goes above the probe curve, as shown at point112 in FIG. 3 b. The transition point between the prove and close phaseis the point at which the close curve goes above the prove curve, asshown at point 113 in FIG. 3 b.

Shapes of the curves are determined by the following principles. At thebeginning of the sales cycle it is assumed that the least amount ofinformation is available regarding all aspects of the customer's needsand the environment surrounding the potential sale. The salesrepresentative must obtain as much detail as possible regarding thesefactors, using the skill of probing. Therefore, probing skills are usedwith maximum focus at the beginning of the sales cycle. As informationis collected and evaluated the process of proving can begin. As probingis completed (i.e. as one moves further through the probe phase), thefocus on proving can increase because we know more about the specificneeds of the customer. Proving skills can then be increasingly used atthe same rate that the skill of probing is decreasing. The degree offocus on probing will continue to decrease throughout the sales cycle asmore information concerning the sales environment is collected, untilthe end of the sales cycle, when the use of probing will beproportionately low.

Use of the proving skill will continue to increase through the salescycle until the customer has been exposed to virtually all features andbenefits of the product. This maximum focus on using the proving skillwill occur in the later stages of the sales cycle. Often, proving willpeak at the point where a demonstration of the product takes place. Fromthis maximum focus the use of the proving skill decreases, until itreaches a minimum at the end of the sales cycle.

The skill of closing entails determining whether the customer has anyreservations about giving their business to the sales representative.Probing techniques must also be used to make these determinations. AsFIG. 3 b shows, the model allows for a degree of probing to be usedthroughout the entire sales cycle. As customer doubts are discoveredthrough the probing process, then proving must be used to allay them. Asthe process of closing intensifies we can see that probing and provingskills must be brought into play, but to a lesser and lesser degree withthe sales cycle nearing an end. As the process of closing continues, theproving and probing processes will decrease.

In summary, the probe curve starts at a maximum at the beginning of thesales cycle and falls off to a minimum at the conclusion. The provecurve is at a low point at the start of the sales cycle and reaches amaximum in the later stages. From there the prove curve falls to anotherminimum at the end of the sales cycle. The process of closing cannotstart until the customer has been fully exposed to the benefits of theproduct (the proving process has reached its maximum); it then increasesin focus until the sale is finalized.

The relative lengths of the three phases can change, but typically theprobe phase is longest, followed by a shorter prove phase and an evenshorter close phase. Usually, the close phase is no more than 10 to 20percent of the total cycle. Proving should not be used too early beforeconsiderable probing has been accomplished. Also, it is not a good ideato start closing too early. The ideal sales cycle is dividedapproximately into 50 percent probe, 35 percent prove, and 15 percentclose. A beneficial feature of implementing the model of the presentinvention is that it can be used to keep the sales representative ontrack with respect to the three basic skills. The salesperson should bereminded if they are tempted to move too quickly from one phase toanother, or to use a specific technique excessively at a point in thesales cycle where it is highly inappropriate. Aspects of the presentinvention, as embodied in a computer based sales automation system,provide such advice to the salesperson.

FIGS. 4 a-4 c shows three examples of sales cycles which have the samecycle length, but different relative lengths of probe, prove, and closephases. FIG. 4 a shows a sales cycle with a long probe phase 201. Thissituation can occur if a customer is unsure of exactly what he needs tofill his requirement—that is, there may be numerous alternativesolutions, and proving cannot begin until a specific solution isidentified. FIG. 4 b shows a sales cycle with an overly long provephase, 202. This kind of situation can occur if a very complex solutionhas to be proven to the customer, which may need extra time andresources compared to normal products. FIG. 4 c shows a sales cycle witha long close phase, 203. This usually happens when a customer finds itdifficult to make a decision. All of the evidence has been presented,and the product has been proven, but the customer is afraid,procrastinating, or having trouble deciding on a specific vendor.

It is important to note that deviations from the ideal situation can,and do, occur, as described by the model. However, the model is flexibleenough to be able to identify and accommodate the abnormalities, and canbe implemented so as to warn the sales representative when appropriateaction may have to be taken.

3. Interactions

Throughout the sales cycle, interactions occur between the salesrepresentative and the customer. Some interactions involve direct verbalcommunication in a face-to-face environment, while others such as faxes,e-mails, letters, etc., involve a “one way” communication, where it isuncertain whether the party to whom the correspondence is directed hasnecessarily received or reacted to it. The model distinguishes betweentwo types of customer interactions—critical and non-critical. Criticalinteractions are those that contribute to the overall knowledge of thespecific sales opportunity, and are important in determining the bestsales strategy for that opportunity. This information may be positive ornegative with respect to the sales representative's case, but evennegative information is useful in understanding the overall backgroundor environment of the sale and adapting strategies to compensate.Non-critical interactions are not specific to any sales opportunity.They may be casual or unimportant but still be deemed worth recording bythe sales representative. In this case, the sales automation systemdescribed here offers the ability to store and retrieve this informationif required, but also separates them from the strategically importantcritical interactions.

In any given sales cycle, the model says that a certain number and typeof critical customer interactions should occur. Also, the modelspecifies roughly when these interactions should occur, in which phase,and which point in that phase. This information can be determined usingpast experience and expert knowledge and is entered in advance for aparticular sales opportunity by, for example, the sales manager. Forexample, FIG. 5 shows a model sales cycle for a small piece oflaboratory equipment that has a value of around five thousand dollarsand an average sales cycle length of three months. The sales manager haselected to allocate the critical customer interactions as follows:

The first interaction is the one that led to the discovery of the salessituation (401). This always involves a direct verbal interaction withthe customer. The only exception to this would be in the event that thesales representative learned of the situation through a source otherthan the customer—but in this case, the representative should alwayscheck directly with the customer to confirm the information.

Next, the customer is sent a complete information package on the product(402). This should occur soon after the initial interaction.

The sales representative then makes a telephone call (403) to thecustomer to establish that product information has been received, andalso to probe on which specific product best fits the customer's needs.This call is made in the early stages of the probe phase.

The next interaction that the sales manager has entered into the modelis a visit by the sales representative to the customer (404). Thisoccurs in the later stages of the probe phase.

A phone call is then made to the customer to set up a time todemonstrate the product (405). This happens in the early stages of theprove phase.

Next comes a demonstration of the product at the customer's facility(406). This of course, again involves a direct face to face meeting ofthe sales representative and the customer.

Shortly after the demonstration, a quotation is sent to the customer(407).

After the close phase has begun, another visit is made to the customer(408).

The final customer interaction occurs when the customer tells the salesrepresentative whether or not they have chosen to purchase the product(409).

The sales manager can set up the customer interactions in a number ofdifferent ways: however, based on knowledge derived from many similarsituations, it can be gauged fairly accurately how many, and what typeof, customer interactions should occur. In order for the model to beeffective when embodied in a computerized sales automation system thenumber and type of interactions is not critically important. The salesmanager can lean toward a model with a bare minimum of customerinteractions and assume that this will then ensure that the salesrepresentative is flagged whenever his activity is less than the definedminimum. Conversely, the model can be set to reflect the exact type ofinteraction that should occur at specifically defined points in thesales cycle. The sales automation system 10 will then alert the salesrepresentative at any point there is a deviation from what is expected(i.e. the activity has not been completed, or the type of activity isinappropriate). It is important to set up the model to define the“expected” critical interactions that should occur, so the salesrepresentative is made aware when he is, or is not, fulfilling the basicrequirements necessary to secure the sale.

4. Information

Throughout the sales cycle, the sales automation program prompts thesales representative to enter information, such as a new activity orcustomer interaction, into the computer. In a preferred embodiment,required information is categorized as follows:

a. Basic information concerning the sales opportunity, such as who thecustomer is, account, and type of product, (as with any other high gradesales automation program).

b. The length of the sales cycle, as perceived by the salesrepresentative. The start of the actual sales cycle is the time that thesales representative finds the sales opportunity and enters it into thecomputer. The computer then prompts the user for the date that heexpects the customer will place the order. From these two dates thecomputer calculates the length of the sales cycle. If the expectedcompletion date changes for any reason, the new completion date shouldbe entered in the program.

c. What kind of interaction has been completed (i.e. visit, phone call,quotation etc.), and when was it completed?

d. What was learned during the customer interaction?

The regular review and analysis of the information gained throughout thesales cycle is critical to the sales representative's overall success.As used herein, “the sales environment” is defined as “all aspects orcircumstances surrounding the sales situation that may have an impact onwhether the sales representative wins or loses the sale”. The modelprovides a knowledge base, derived from a structured set of questionsregarding key aspects of the sales environment. These questions arequite generic to a wide range of industries, but can be completelycustomized by the sales manager. The information is categorized as twotypes:

(i) Information required to completely understand the sale—in thisinvention, the sales environment. The only way to establish whether thesales representative is totally aware of the sales environment is forthe sales automation system to routinely and automatically ask questionsthat should be answered by the salesperson. Aspects of the salesenvironment are: Who are the decision-makers? Who are the competitors?Has the customer sufficient funds to purchase my product?—and so forth.The automation system 10 prompts for this information by asking specificquestions of the sales representative, (i.e., “who are the decisionmakers and please name them?”). This information is obtained using theskill of probing, which the model allows to occur throughout the entireduration of the sales cycle. Other information that is part of the salesenvironment is obtained using the proving and closing skills. Thequantity and quality of information gained is an indicator of thesuccess of proving.

(ii) Information required to assess one's progress in the sale. Thisassessment is obtained by evaluating, “how well am I doing at this pointin the sales cycle?” This can be answered by asking, “how well am Iproving, and how well am I closing” The sales automation system obtainsthis assessment by posing questions such as, “to what degree have youproved the value of your product to decision maker number 3?” or “haveyou made a trial close at this point in the close phase?”. The answer tothis type of question is solicited from the user by asking them tochoose, for instance, from one to five possibilities, ranging from “notat all” to “completely”. Input is requested regarding the salesrepresentative's assessment of the probability of winning this sale.This assessment must be made when first entering the opportunity, thoughit is normal for the assessed probability to change a number of timesduring the sales cycle. Again, the sales representative should be awareof this eventuality and must record any changes in the sales automationprogram to reflect the new situation.

In preferred embodiments, the sales automation system enables comparisonof the information entered by the sales representative to that which isstored in the sales model. With reference to FIG. 1, the user enters,via user interface 130 information starting at the beginning of thesales cycle, when the sales opportunity has been discovered, andcontinuing throughout the sales cycle, as subsequent interactions withthe customer occur. The sales model is stored by sales model module 100,and has been configured with the four components of time, selling skill,activity, and information for the product or service the salesperson isselling. Any deviation occurring at any point in the sales cycle betweenthe information entered by the salesperson and that which is containedin the model, is called a “gap”. The interaction monitor 160 comparesactual interaction activity that has been entered as completed by acertain time in the actual sales cycle with the interaction activitythat should have been completed by that time according to the salesmodel The information monitor 150 compares the actual informationentered as gained at a certain time in the actual sales cycle with datain the sales model (a simple example would be if the salesrepresentative entered an opportunity for a certain product andestimated the sales cycle length to be six months, while the averagesales cycle was stored in the model as nine months, thereby creating agap of three months).

The gap information is passed to a rules processor 140 which isprogrammed to pass an intelligent response 142 to the user based on thisinformation. In the above case the intelligent response may be, “youhave entered a six month sales cycle, the average is nine—you are lateinto this sale and have some catch up work to do”. Another intelligentresponse would ask the sales representative to check the expected datefor the sale, in the event that a data entry mistake had been made.

Another example of the intelligent response would be if one third of theway through the sales cycle four customer interactions have occurred andrecorded in the computer by the sales representative. The sales modelindicates that at such point in the sales cycle six interactions shouldhave occurred. The gap is two interactions that have not occurred. Therules processor 140 processes the gap information and feeds a message(i.e. “at this point you need to try to make two more customerinteractions”) back to the user (sales representative). The response canalso be dependent on what the current phase of the sales cycle is, andhow much information has been collected. For instance, the response tothe salesperson can be as encompassing as, “you are half way through theprobe phase but have not yet identified the decision makers—yourcustomer interactions are fewer than would be expected at this time—itis time to see the customer”.

A further example is the situation where a sales representative, at theend of the probe phase, should have entered the answers to six questionsconcerning the sale, but in fact has only answered two of the six. Thegap is four questions not answered. The rules processor could beprogrammed to pass an intelligent response to the user suggesting thatthe four questions have to be answered before the sales representativeis “allowed” to pass to the prove phase. This advice could also beforwarded automatically to the representative's manager. There are manypossibilities for the types of responses that can be initiated by therules processor, given the circumstances in which the gap information isgenerated. In general, the intelligent response is constructed to advisethe salesperson to modify activities or information gathering so the neteffect is to minimize the gap.

There are many different ways of presenting intelligent responseinformation to the user. The most direct way is to present theintelligent response on the screen of the salesperson's computer 182,immediately after the salesperson has entered information from customerinteractions associated with an existing opportunity. This kind ofresponse occurs in real time, the only delay being the time the computerneeds to calculate and present the response to the user.

Another way is through routine summary reports, which can be sent to thesales representative automatically via e-mail by the sales assistanceprogram. These reports, unlike the direct method of presentinginformation on one opportunity in real time on the computer screen,gather summary information on all of the opportunities that the salesrepresentative may be working on at any given point of time.

Assigning Probabilities

In preferred embodiments of the invention, the automation system 10 isconfigured to generate, based on user inputted information, aprobability indicator for sales opportunities. For every salesopportunity there is a certain probability that the sales representativewill be successful in winning the sale over the competition. It is, fora number of reasons, extremely important to try to quantify thisprobability of success—the most important one being the ability toforecast accurately. A company needs to continually plan for the future,and a vital part of this plan is an assessment of the revenue that willbe generated by the sales team in the future.

It is important that the sales representative determines the chances ofsuccess, not only upon initial discovery of the sales opportunity, butalso throughout the sales cycle, as the sale progresses. This exerciseis necessary because the dynamics of a sale shift constantly throughoutsales cycle. This means that the total forecasted company revenue isalso constantly changing. Therefore, the salesperson should regardassigning probabilities to his opportunities very seriously, and shouldupdate this information anytime that a significant change occurs in thesales cycle.

There is another important reason to review opportunities regularly onthe issue of probabilities. This process will focus the salesperson tocritically evaluate each situation and the issues surrounding the sale,and will trigger changes in strategies if needed, to improve the chanceof success.

The sales representative also needs to prioritize opportunities underhis responsibility so as to answer the question, “where shall I bestspend my time?”. This is not an easy task, and many factors must betaken into consideration. The probability of the sale happening is themost important component in determining where the representative shouldbest use their most important resource. As will be explained in greaterdetail below, the sales automation system 10 of the present inventionintegrates the assessed probability with the skill phase (i.e. probe,prove or close of the sales cycle at that time) and enables the computerto assign a priority to the opportunity.

Obviously, determining the probability of success in a sale is not anexact science—in fact, it is one of the most difficult parameters toquantify. It also creates work for the person in charge of filteringmonthly forecasts from the sales force—sales people differ tremendouslyin the way they forecast. Forecasting always brings out the pessimisticor optimistic side of our nature, and it is therefore quite possible fortwo excellent sales professionals to be at opposite ends of thespectrum. It is a challenge to the sales automation system to provide amethod of deriving probabilities that eliminates such deviations fromone salesperson to another.

Most sales automation systems merely ask the question “what percentageprobability is there that we will get this sale—answer between zero andone hundred percent”. It will be appreciated that answers to thisquestion such as 37%, 45% or 65% rarely have the exactness that theyseem to imply. The most that can be said is that the 37% and the 45%values may likely mean the same thing, as may the values of 45% and 65%.However the values of 37% and 65% probably reflect a differentprobability of success. In other words, in the complex sales process itis impossible to forecast to this level of certainty. Experience showsthat asking for assessments categorized to increments greater than sixis often pointless.

The present invention relies on the salesperson's ability to judgeaspects of the sale that determine the probability of winning the sale.The objective is to ask for his or her “gut feel” about whether the salewill be won or not. Although a numerical value of the probability isdesired, under the present invention the salesperson is not directlyasked for the number, because it will not be consistent or accurate.Part of the solution lies in asking the salesperson easy, butpenetrating, questions about crucial elements of the sale and to limitthe number of possible replies. The other part of the solution is toconstruct the probability value from two totally unrelated questionsconcerning the sale, preferably questions that need answers innon-numerical terms.

The extremes of assessing probabilities are, “this customer isdefinitely buying, and is also buying my product”, and “he is probablynot going to buy anything, and if he does he will definitely buy fromthe competition”. In between we have the challenging situation of, “thiscustomer may buy something and we have an equal chance with thecompetition that they might buy from us”.

The above examples illustrate a possible method to make it easier forthe sales representative to approximate the probability of securing thesale. There are two basic questions to consider here; the first being,is the customer going to buy anything at all, even from the competition?A number of factors may determine this, and the sales representativemust be prepared to look in detail for any one of them. For example,does the customer have the ability to finance what he wants? How willcorporate politics influence the ability to get what he wants? Is thiscustomer being too optimistic that upper management will allow him to goahead with this project? The sales representative has to be acutelyaware of these issues to determine the chances the sale will actuallycome to fruition. In this case the question asked of the salesperson is,“Will this sale happen?”

Aside from the question of whether the sale will happen, the salesrepresentative has to evaluate the chances that the customer will choosehis product over the competition. Factors affecting this decision willbe things such as the relationship between the representative and thecustomer—how effectively is he selling the customer on his product. Howeffective is the competition. The question asked here is, “Will we getit?”

Probability is determined by the answers to “Will the sale happen?” andif it does, “Will we get it?” If the answer to each question is one ofthree alternatives—High, Medium, or Low chance, then, as can be seen inFIG. 6 a, the possible answers lie on a three by three grid 210, with atotal of nine possibilities. This is called the probability matrix. Asillustrated in FIG. 6 b, each unique point on the matrix represents aunique combination of the answers to the two questions, “Will ithappen?” and “Will we get it?”, and is called the probability index. InFIG. 6 b, the probability indices are labeled from one to nine.

Many sales managers are still, however, accustomed to seeingprobabilities expressed as percentages. Percentage values can beassigned to the individual probability indices, as shown in FIG. 6 c.This exercise does not have to be executed with mathematical rigor inorder to be useful. One method used here is to assume that an answer ofHigh represents a probability value of 80%, Medium is 50%, and Low is20%. Each point on the matrix is the product of the answers to “Will ithappen” and “Will we get it”—High, Medium or Low. Multiplying theprobability values and scaling the answers with some approximationsresults in the five probabilities of 10%, 15%, 25%, 40%, 60%, and 80%.

Determining Priorities

According to preferred embodiments of the invention, the salesautomation system 10 is configured to determine a time-based priority toassign to a sales opportunity by taking into account both theprobability that the sale will occur, and the location (time-wise)within the sales cycle for that sales opportunity. At any time, a salesrepresentative may be working on dozens of sales opportunities, varyingin sales cycle length, and at different points in their sales cycle. Anillustration of this is shown in FIG. 7, which shows a time-line chart212 of just twenty opportunities occurring over a one year period. Thesales cycles range from a few weeks to many months. In the illustratedexample, it can be seen that at the beginning of March, the salespersonhas ten opportunities in the probe phase, zero in the prove phase, andtwo in the close phase. At the beginning of July, he has fiveopportunities in probe, eight in prove, and two in close. At the end ofOctober, he has one opportunity in probe, three in prove and eight inclose. Obviously, if the salesperson has one hundred opportunitiesinstead of twenty, it is difficult to decide where to allocatetime—considering that each of the skill phases should be givenapproximately the same attention. A method of determining priorities, isneeded which can be used to sort a list of opportunities so that thesalesperson can rigorously work through the list from top to bottom,confident that each opportunity is being handled effectively through itsentire sales cycle.

Novel aspects of the present invention are directed towards therecognition that the assessed probability of a sale happening should notbe the only factor in determining the priority a sales representativeshould use when working through a set of opportunities. That is thetechnique that is used in most sales automation systems today, andimposes the danger that the salesperson works only on opportunities thatare near the end of the sales cycle, in their close phase. The point oftime in the sales cycle should also be taken into consideration. Theskill phases provides a way to “normalize” the sales cycles of widelydifferent durations—the skill phases (probe, prove, and close) can beused to provide an extra dimension that works with probability toprovide a useful priority value for the opportunity. This concept isillustrated with examples shown in FIGS. 8 a and 8 b.

FIG. 8 a shows a situation where the sales representative has graded anopportunity in the probe phase (the initial stages of the sales cycle).On the probability matrix “Will it happen?” has been assigned high, and“Will we get it?” has also been assigned high. As this is in the earlystage of the sales cycle, there is still time until the customer buyssomething. There is also still a chance that the competition could makea breakthrough in the time left, so the advice to the salesrepresentative by the automation system 10 would be, “you are veryconfident but don't be too complacent—do enough to protect this salefrom falling to the competition”. A priority 2 has been assigned—not apriority 1. In other words, as this is early in the sales cycle and thecustomer is highly favoring the product, the sales representative mustbe vigilant but does not need to give the opportunity urgent attention.

FIG. 8 b depicts a situation where the same probability has beenassessed, now in the close phase. At this point there is little time forthe competition to intervene. The message to the sales representative is“you are very confident, close this sale and move onto the next one”. Apriority of 1 has been assigned—it is important to close highprobability business before working on other opportunities. The examplesof FIGS. 8 a-8 b show that even though the sales representative hasassigned the same probability in each case—because of the differentpoints in the sales cycle (skill phase probe versus skill phase close),different priorities are assigned. In preferred embodiments of the salesautomation system 10, even though the salesperson does not change theprobability value through the sales cycle, the computer willautomatically update the priority value based on the passage of time.

As in the examples of FIGS. 8 a-b, it is possible to construct a nine bythree matrix 214 (FIG. 9) showing the relevance of the probability indexto the phase of the sales cycle. For each probability index/skill phasecombination it is possible to assign three pieces of information thatare of value to the sales representative:

(1) A simple explanation 216 of what kind of skill phase to expect (i.e.Thorough Probe, Hopeless Close etc.).

(2) An advice message 218 that recommends a strategy based on theassessed probability and the phase of the sales cycle, (i.e. “Sale islikely to happen. However, don't get complacent and spoil your leadingposition”).

(3) A priority 220 to address this particular opportunity. In theillustrated example four priorities are assigned, ranging from 1 to 4, 1being the highest. A special description of “leave it alone” has beengiven to priority 4. A special category of “breakthrough needed” is alsoassigned to the special case where the sale will definitely happen, butthe sales representative has a low chance in the later stages of thesales cycle.

The three pieces of information listed above, which are derived from thematrix 214, are used to provide valuable feedback to the salesrepresentative as the sales cycle progresses.

Another way to graphically represent the concept that priority must bededuced from a probability taken in the context of skill phase is shownin FIG. 10. The regular probability matrix 210 is shown, with axes of“Will it happen?” and “Will we get it?”. In addition, a third axis isshown, which is actually the sales cycle divided into the three phasesof probe, prove, and close. This forms a “priority cube” 222, composedof twenty seven unique elements, each with specific information onpriority and intelligent response.

An overview of the concepts incorporated into preferred embodiments ofthe invention having been provided above, an example of the operation ofthe sales automation system 10 will now be provided.

Generally, one of the first steps will be for a sales manager is to setup the sales model parameters in the computer system. This can beachieved using a graphical representation 224 of the sales cycle as theuser interface 130 on the sale's managers computer screen as shown inFIG. 13. Key elements of the sales model are shown on the screen (i.e.the probe, prove, and close phases 1301, the types of criticalactivities 1302, and triangle icons 1303, each of which represent a unitof time along the sales cycle). When a triangle icon 1303 is clicked, adialog box allows the user to enter a critical activity from a rangedisplayed. The critical activity can also be tagged as mandatory ordiscretionary (which can be shown as different colors on the displayscreen). In addition, the average length of the sales cycle is alsoentered in an average length field 1304, and a product field 1305 isfilled in to identify the product to which the sales cycle isapplicable. In the illustrated embodiment, the probe, prove, and closephases are assigned 50%, 35%, and 15% of the sales cycle, respectively.It would also be possible to allow the sales manager to set up otherrelative durations for the skill phases.

As noted above, a user interface 130 allows a user such as a salesrepresentative to enter information about customer interaction for useby an interaction monitor 160, and to enter other information for use byan information monitor 150. When a sales representative identifies asituation where a customer has started the buying process, he enters thesales opportunity into the sales automation system 10. An example of thegraphical user interface 226 used to log new sales opportunities intothe computer program is shown in FIG. 12. The sales representative isprompted to select from a drop down list an “activity type” 1607 of theinitial interaction with the customer, and the date 1608 of the initialinteraction. The date that the opportunity is entered is defined as thebeginning of the sales cycle. Qualifying questions 1601, (for example“What is this customer's purchasing intent?”) are presented to the salesrepresentative, along with possible selectable responses (for example“Plans to Buy”, “Does Not Plan to Buy” and “I Don't Know”). In theillustrated example, if the sale representative indicates that thecustomer plans to buy, he is presented with the questions “Will ithappen?” 1602, followed by, “Will we get it?” 1603, and the salesrepresentative is prompted to enter a response of “High”, “Medium” or“Low” to each of these two questions. The sales representative is thenrequested to enter the date that he expects the sale will conclude, as awin or a loss, through the question “When will it happen?” (month andyear), 1604. Based on the entered answers to the “Will It Happen” and“Will We Get It”, the computer brings up the probability matrix, 1605,with the appropriate probability index highlighted. Beneath theprobability matrix the system displays information, 1606, derived fromthe priority table 214 shown in FIG. 9.

Based on the entered conclusion date, the system calculates the lengthof the sales cycle the representative has entered. This is referred toas the “actual” sales cycle (FIG. 3 a). If the sales representative islate in recognizing the start of the customer's buying process, theactual sales cycle will be shorter than the customer's sales cycle (FIG.3 a). The model sales cycle stored in the computer (as predetermined bythe sales manager) is the average sales cycle for the product inquestion. If the length of the actual sales cycle entered differs fromthe average sales cycle (FIG. 3 a) as stored by the model, the rulesprocessor 140 warns the user that an abnormal situation exists and thatspecial strategies may have to be implemented. The aspects of the modelsales cycle which are time related, such as cycle length, activities,probe, prove, and close phases, are scaled to the actual sales cyclelength. This is exemplified in FIG. 13, where a model sales cycle 1501of six months is scaled to the actual sales cycle 1502 duration of fourmonths 1502.

The automation system 10 is preferably configured so that If, part waythrough the sales cycle, the sales representative discovers that thecustomer is, for whatever reason, prolonging the purchasing decision,then he will change the expected conclusion date for the sale. The salesautomation system recalculates the sales cycle length within the newparameters and automatically scales the features of the model to the newlength.

In one preferred embodiment, a new customer interaction is recordedthrough a “new interaction” graphical user interface 228, generated bythe software of the sales automation system, shown in FIG. 14. The salesrepresentative is first prompted to identify at field 1801 if the newinteraction is “critical”—that is, does it contribute to the knowledgeof the progress of this specific sales opportunity. This invention isdifferent to other sales automation systems which do not make thedistinction between critical and non-critical information. If such adistinction is not made, views that show opportunities and associatedinteractions become “cluttered” with extraneous information (i.e. “Icalled the customer but he was not there”). Non-critical information canbe recorded by answering “no” to the question 1801, in which case thescreen changes to remove the list of critical activities 1804 to allowthe representative to type in details of the non-critical interaction.If the interaction is critical, an activity type 1802 and shortdescription 1804 of the interaction may be selected along with the dateand time 1803 of the interaction. The sales representative may input anyother details that they see fit in optional field 1805.

With respect to displaying interactions that have been entered, thesales automation system, according to preferred embodiments of theinvention, has the ability to associate, or link, customer interactionswith a specific sales opportunity, and therefore with a sales cyclebelonging to that opportunity. This makes it possible to easily view aspecific opportunity and its historical interactions, without viewinginformation from other opportunities, including those that are past orclosed, belonging to the same contact. FIG. 15 is an example of agraphical user interface based on this type of database structure, andshows the account 1701 the customer or contact 1702 and the salesopportunities 1703 as they appear in a summary “view” in the program.Each line in the view is a document that can be opened to provide moredetailed information. In FIG. 15, the account is level one 1701. Thecontact belonging to the account is shown indented at level two 702. Acontact may have a number of current or past opportunities, which showup indented under the contact, as level three, 1703. In the displayshown in FIG. 15, critical interactions pertinent to a particularactivity as linked specifically to that opportunity are shown. In thisway, it is possible to see the chronological sequence of customer/salesrepresentative interactions that occur through a given sales cycle.Sometimes a contact may have more than one opportunity at one particularpoint of time. By linking an activity with the opportunity to which itis relevant, it is possible to see the exact sequence of activities orinteractions that influence the specific opportunity, without theclutter of information from other situations or past history. This isshown in FIG. 15, where the interactions for a specific opportunity,1704, appear under that opportunity in chronological order, essentially“mimicking” the horizontal time axis of the sales cycle model. In thisexample the contact “Kelly Bundy” has two opportunities (IBO is an“Identified Business Opportunity”), with interactions that have occurredfor each.

FIG. 16 shows an informational display screen layout 230 that can begenerated by the sales automation system 10 which displays the keyparameters of a subject sales opportunity, as entered through the entryprocess described with reference to FIG. 12. These parameters are calledthe “IBO essentials”. The screen 230 is presented to trigger the salesrepresentative to update essential information in the event thatcircumstances have changed. (As indicated in FIG. 16, each salesopportunity is assigned a unique number 1901. This number is used tolink and track activity associated with the opportunity during the salescycle.)

Using the buttons 1902 the representative is able to update theprobability matrix for the opportunity, which is displayed as a ninepoint matrix, 1904, and using fields 1903, update the expected date forthe sale to happen. A representation of the sales cycle and the currentphase 1905 are shown. Also shown is a vertical bar 1907 indicating thecurrent position in the sales cycle. The priority (1908), as determinedby the nine by three matrix 214, is also shown, as is the adviceinformation 1909 obtained from the same matrix. Priority and adviceinformation are updated as the representative changes the “Will ithappen?” and “Will I get it?” buttons. This information also changesautomatically in the event that time has passed to put the opportunityinto another phase of the sales cycle, even though the salesrepresentative may not have input any data. This is because the assessedpriority is affected by which phase one is at within the sales cycle.

The graphical user interfaces displays used to elicit additionalinformation from the sales representative about a sales opportunity willnow be discussed with references to FIGS. 17 a-17 c, which show exampledisplays for entering information gathered using the probing, probingand closing skills, respectively. The “sales environment” encompassesall aspects and circumstances surrounding the sale that ultimatelyaffect whether the salesperson will win the sale or not. The salespersonmust make it a primary objective to obtain information that allows himor her to understand the sales environment. The sales environmentusually changes as the sales cycle progresses, and therefore thisinformation gathering activity must constantly be used. Different typesof information are required, depending on the phase of the sales cycle.

Under the sales model of the present invention, the probing skill can beused throughout the sales cycle, though the majority of probing is donein the probe phase. When probing, it is important to gather as muchinformation as possible about the factors that influence the sale. Thisinformation may be positive or negative, with respect to the motives ofthe sales representative. Negative information would be, “thecompetition is ahead at this point in the sale”. Without thisinformation however, the representative cannot change strategies torectify the situation. FIG. 17 a shows a computer screen for enteringinformation gathered using probing. By clicking on the “probe” tab,2001, the user is presented with a series of probe questions. In theillustrated embodiment of the sales automation system incorporating thisinvention the questions are generic, but in other embodiments thequestions can be customized for a particular product or industry. Inthis graphical user interface the user merely has to choose betweenpossible answers to the questions from drop-down lists. For instance,the answer to “What are the chances of the customer receiving funding?”can be either “Good”, “Poor”, or “Unknown”. To fill in this type ofinterface requires little time for the user. The user input screen 232also asks the sales representatives who the decision-makers in this saleare (field 2004), how influential they are (field 2005), and what isimportant to them in making their decision (field 2006). The computerinterface allows the sales representative to enter or change informationany time through the sales cycle, if the environment surrounding thesale changes, and not just during the probe phase.

FIG. 17 b shows a screen 234 of the questions presented in respect ofthe proving skill of the sales representative, and is activated when theprove tab 2002 is pressed. These questions ask the sales representative“to what degree has he proved?”. In the illustrated embodiment, themajor decision-makers that have been identified from the probe phaseinput screen 232 are listed. The user is required to click anappropriate button 235 indicating the “degree of proving” required foreach decision-maker, with respect to the determining factor that affectstheir decision. There are five options, ranging from “not convinced” to‘convinced”. For instance, in FIG. 17 b, Cyril Chaput, the economicdecision maker, is concerned about price, his influence on the decisionis high, and the relationship he has with the sales representative isgood. The sales representative has clicked a four out of five, meaningthat he thinks he has convinced Cyril Chaput to the 80% level.

FIG. 17 c shows a screen 236 of the questions presented in respect ofthe proving skill of the sales representative, and is activated when theclose tab 2003 is pressed. The sales representative is asked if he hasattempted to close the sale (this is called a trial close). If not, theprogram will remind him throughout the close phase that he should do so.If he has attempted to close the sale he must answer the question “isthe customer prepared to buy your product?”. If the answer is yes, thesales representative is given the option of closing the opportunity. Ifthe answer is no, then the sales representative is required to state thebarriers to winning the sale; these could be issues such as price,service, or technical performance of the product. In the illustratedembodiment, the barriers are chosen from a drop down list of possiblegeneric choices, however in some embodiments they may be user definable.The input screen 236 also prompts the sales representative to determinea strategy to handle the customer's objections (field 2007). Thesestrategies may also be chosen from a drop down list. Once the strategyhas been entered, the sales representative will be prompted to indicatedif the strategy has been implemented. If it has, this represents thebeginning of another trial close loop. The essential details of eachtrial close are stored for review. The sales representative will keepmaking trial closes, using strategies to overcome the barriers to thecustomer making a decision, until the sale is won or lost.

In some embodiments, the sales automation system measures the number oftrial closes made against time, and advises the user if trial closesneed to be made. In addition, as the process of closing starts roughlyhalf way through the prove phase, the sales automation system directsthat at least one trial close should be made in the last half of theprove phase. The system may display a message to reinforce the pointthat it is bad to try to close a sale too early, if the user attempts toopen the close tab 2003 before the maximum point in the proving curvehas been reached.

Intelligent Response Mode

In one preferred embodiment of the invention, the sales automationsystem 10 is configured to provide intelligent responses to the salesrepresentative, as explained in greater detail below.

The sales automation system is configured to determine based onpre-defined rules, and based on information entered by the salesrepresentative, computer generated answers for the “IBO Essentials”(namely, answers to the questions “Will it happen?” and “Will we getit?” and “When will it Happen”), compare if the computer generatedanswers match those provided by the sales representative, and based onsuch comparisons, provide some feedback or advice to the salesrepresentative. As the IBO Essentials are used to determine probabilityand priority, the ability to check the salespersons assessment forcorrectness is a desirable feature. The detailed information gatheredthrough the interfaces shown in FIGS. 17 a-17 c about the salesenvironment can be used to double check the sales representative'sassessment of the IBO essentials.

FIG. 18 shows an example of a graphical user interface 238 for operationof the program in an intelligent response mode. The interface is similarto that of FIG. 16 in that it has an IBO essentials entry/display area2201 (“Will it happen?”, “Will we get it?”, and “When will it happen?”),a probability matrix display area 2202, and a diagram of the sales cycle2203 showing the current point in time by means of a vertical bar 2209.Also displayed is the priority value 2204 derived from the salesrepresentative's assessment of the IBO essentials. In the probabilitymatrix display 2202, the sales representative's assessment of “high” forthe question “will it happen” and “medium” for the question “will we getit” is illustrated by box 240.

In addition to the information shown in FIG. 16, in “intelligentresponse mode”, the computer calculates its own values of “Will ithappen?”, and “Will we get it?” (Medium and low in the illustratedexample) and this is presented on the probability matrix 2202 by anotherindex block 242. As the sales automation system calculates a differentprobability than the user in the illustrated example, the priority 2205that it calculates in this case is also different and is shownunderneath the user's priority.

In a message section of the graphical user interface 238, the messagesare quite different to those shown in FIG. 16, which are derived fromthe twenty-seven entry priority table 214 only. In FIG. 18, theintelligent response mode provides more context sensitive advice 2206,2207, 2208 to the salesperson.

As will be explained in greater detail below, the first message field2206 is displayed in response to differences in computer generatedprobability and the user's probability. The second message field 2207 isdisplayed in response to differences between the computer and userassessments of the question “Will it happen?”. The third message field2208 is the sales automation systems detailed comments based on itscalculation of the probability index from the probe, prove, and closequestions concerning the sales environment.

The criteria used by the sales automation system 10, and in particularthe rules processor 140, to determine answers to the questions “Will itHappen” and “Will we get it” based on data entered by a salesrepresentative regarding the sales environment and the stored salesmodel will now be explained in respect of one preferred embodiment ofthe invention.

Turning firstly to the question of “Will it Happen?”, in one embodiment,the sales automation system assigns an answer to such question based onthe sales representative's answers to the two questions: 1) What are thechances of the customer receiving funding? (Possible answers: Certain,Fair, Low and Unknown); and 2) What is the customer's level of need?(Possible answers: Urgent, Normal, Low or Unknown) (recall that suchquestions are part of the informational “proving skill” questions, asshown in FIG. 17 a).

Table 1 shows how the various combinations of the answers to these twoquestions as provided by the sales representative are used by the salesautomation system to determine the value of “Will it happen?” If theanswer to either question is “Unknown” then it is categorized as Low inthe table.

TABLE 1 Chance of Level of Will It Funding Need Happen? Certain UrgentHigh Certain Normal High Certain Low Med Fair Urgent Med Fair Normal MedFair Low Low Low Urgent Med Low Normal Low Low Low Low

As mentioned above, the “Advisor” message 2207 of the display screen 238is determined by the sales automation system in response to differencesbetween the sales representative's answer to the question “Will itHappen” and the computer assigned answer. The table shown in FIGS. 19a-19 b demonstrates how the rules processor 140 uses intelligentresponse technology to construct messages to the user pertinent to “Willit happen?”, based on the answers that the salesperson has provided onthe questions of funding and needs. Column 260 shows sample “Advisory”’messages that will be issued based on the various possible combinationsof: the sales representative's answer to “Will it Happen” (column 244);the computer determined answer to “Will it Happen” (column 246); and thesales representative's answers to the questions noted above concerningavailability of funding (column 248) and level of need (column 250). Forexample in one of the cases 262 shown in FIG. 19 a, the salesperson hasanswered “Will it happen?” as Low. The computer has calculated “Will ithappen?” to be Med because, according to the salesperson, funding isCertain while the level of need is Low. In this case, the computer(referenced as the Sales Advisor) is able to present the message,“Advisor alert: funding will happen, despite a low need?” Thischallenges the salesperson to check his or her evaluation of the salesenvironment. Maybe the chances of funding are Low, maybe the customer'sneed is higher than thought—or maybe, on reflection, the “Will ithappen?” is Medium.

Turning now to the analysis of the question “Will we get it”, in apreferred embodiment there are six factors that affect the computer'sdetermination of “Will we get it?”, namely: (1) results from theinteraction monitor 160; (2) results from the information monitor 150;(3) the degree of competitive pressure; (4) the degree that the productmatches the customer's needs; (5) How the price of the product matchesthe customer's budget; and (6) The sales representative's relationshipwith the decision makers. It will be recalled that the salesrepresentative is prompted to answer questions regarding factors (3),(4) and (5), as indicated in FIG. 17 a, and also to provide relationshipinformation, as indicated in FIG. 17 b.

In classifying the answer to the question “Will we get it?” as one ofthree values: High, Medium or Low, each one of the factors (1)-(6) isconsidered to determine its contribution to the final answer to “Will weget it?” in terms of High, Medium or Low. The answers are then groupedtogether to get the collective categorization for the final answers. Themethodology used to assign a High, Medium of Low in respect of each ofthe factors (1) to (6) will now be explained in accordance with oneembodiment of the invention as follows:

(1) Interaction Monitor Factor:

The interaction monitor 160 counts the number of critical interactionsthat have occurred between the salesperson and the customer, andcompares this against the number that should have occurred as defined bythe sales model. This embodiment takes the ratio of interactionscompleted divided by interactions required. If all or more interactionshave been completed, this ratio is 100% or higher and the Interactionmonitor factor (1) is assigned a “High” contribution to “Will we getit?” (the more work that has been done in the sales, the higher thechances of winning it). If the ratio is less than one hundred percentbut greater or equal to fifty percent, the contribution to “Will we getit?” is assessed to be Medium. If the ratio is lower than fifty percent,the contribution to “Will we get it?” is assessed as Low.

(2) Information Monitor Factor.

The information monitor deals with the acquisition and quality ofinformation. Acquisition of information is determined by the number ofprobing questions that have been answered at a point in the sales cyclecompared with the number that should have been answered at that timeaccording to the sales model. The quality of information is judged bythe value of the answer to the question and is judged separately fromwhether the sales representative probed for the answer or not.

In a preferred embodiment, the sales automation system divides theamount of information gathered by the amount of information required. Ifall information has been obtained, this ratio is 100% and theinformation monitor factor is assigned a “High” contribution to “Will weget it?” (the more information that has been obtained in the sale, thehigher the chances of winning it). If the ratio is less than one hundredpercent but greater or equal to fifty percent, the contribution to “Willwe get it?” is assessed to be Medium. If the ratio is lower than fiftypercent, the contribution to “Will we get it?” is assessed as Low.

(3) Competitive Pressure Factor.

If the level of competitive pressure has been entered by the salesrepresentative as high in the sales situation, the contribution of thecompetitive pressure factor to “Will we get it?” is assessed as Low. Ifthe level of competitive pressure has been identified by the salesrepresentative as is medium in the sales situation, the contribution to“Will we get it?” is assessed as Medium. If the level of competitivepressure has been identified by the sales representative as low in thesales situation, the contribution to “Will we get it?” is assessed asHigh (if the competitors are weak, we have a greater chance to win thesale). If the level of competitive pressure is identified as “unknown”,the contribution to “Will we get it?” is assessed as Low.

(4) Product Matching the Customer's Needs Factor.

If the product has been identified by the sales representative as havinga high degree of matching the customer's needs, the contribution offactor (4) to “Will we get it?” is assessed as High. If the product hasbeen identified as having a medium degree of matching the customer'sneeds, the contribution to “Will we get it?” is assessed as Medium. Ifthe product has been identified as having a low degree of matching thecustomer's needs, the contribution to “Will we get it?” is assessed asLow. If the product's match to the customer's needs is unknown, thecontribution to “Will we get it?” is assessed as Low.

(5) Matching the Customer's Budget Factor.

If the price of the product has been identified by the salesrepresentative as matching the customer's budget, the contribution offactor (5) to “Will we get it?” is assessed as High. If the price of theproduct is higher than the customer's budget, the contribution to “Willwe get it?” is assessed as Medium. If the price of the product is muchhigher than the customer's budget, the contribution to “Will we get it?”is assessed as Low. If the customer's budget is unknown, thecontribution to “Will we get it ?” is assessed as Low.

(6) Relationships with Decision Makers Factor

If the sales representative's relationship with the decision maker isgood, then the contribution of factor (6) to “Will we get it?” isassessed as High. If the relationship is OK, then the contribution to“Will we get it?” is also assessed as High. If the relationship is bad,then the contribution to “Will we get it?” is assessed as Low. In theillustrated embodiment of the invention, there are three separatedecision makers, the economic, the technical and the user. Therelationships as defined by the sales representative can be combined toget the overall contribution for factor (6) to “Will we get it?” Forexample, if the sales representative has identified that hisrelationship with each of the three decision makers is at least “OK”,then the contribution of factor (6) is “High”. If the salesrepresentative has identified that he has a bad relationship with onlyone decision maker, but a good relationship with at least one of theother two, then the contribution of factor (6) is “Medium”. If the salesrepresentative has identified that he has a bad relationship with atleast two of the decision makers, then the contribution of factor (6) is“Low”. In some situations, the user inputted degree of influence foreach of the decision makers (see field 2005, FIG. 17 a) may be used todetermine the contribution of factor (6). For example, in the event thatthe sales representative has indicated that he has an OK relationshipwith two decision makers and a bad relationship with the third, thelevel of influence of the decision makers could be taken intoaccount—for example, if the “bad” relationship decision maker has lowlevel of influence, the contribution of factor (6) may be assessed as“Medium”, whereas if the “bad” relationship decision maker has a highdegree of influence, then the contribution of factor (6) may be assessedas “Low”.

Thus, by following the methodology set out above, the sales automationsystem 10 can assess for each of the six factors noted above a High,Medium or Low contribution to the issue of whether the subject companyor the competition will win the sale. These results must then bedistilled down to a single set of High, Med and Low values that reflectan amalgamation of the six factors. In the preferred embodiment, theresults are considered differently depending on whether the sales cycleis in the probe phase or in the prove or close phase.

In the probe phase, factor (6), namely relationships with the decisionmakers is ignored, and only the “High”, “Low” or “Medium” assessmentscalculated in respect of the other five factors (1)-(5) are considered,and each of these five contributing factors are assumed to contributeequally to the possibility of “Will we get it?” In one exemplaryembodiment, if at least three “Highs” and no “Lows” (for example, thecombined assessments of H-H-H-H-H; H-H-H-H-M; or H-H-H-M-M, whereH=high, M=medium and L=low) have been cumulatively assessed in respectof the five factors, then “Will We Get It?” is assigned a “High”. If theforgoing is not the case, and at least three “Highs” and at least one“Low” (for example H-H-H-H-L; H-H-H-M-L; or H-H-H-L-L) have beenassessed, or if the five factors have received assessments of H-H-M-M-L;H-H-M-L-L or H-M-M-M-M, then “Will We Get It?” is assigned a “Medium”.If no more than one “High” and at least one “Low” have been cumulativelyassessed for the five factors, or if no “Highs” have been assessed, then“Will We Get It?” is assigned a “Low”. Special conditions may also beset for some combinations—for example the combination of two “Highs” andthree “Mediums” (H-H-M-M-M) could cause a further determination to bemade as to whether either, but not both, of the information monitorfactor and interaction monitor factor where assessed as high, in whichcase a “Medium” would be assigned to “Will we Get It”, and in the eventthat neither of the information monitor or interaction monitor factorswere assessed as “High”, then a “Low” would be assigned to “Will we getIt”.

When determining the answer to the question “Will we get it” in theprove and close phases, all of the six factors (1)-(6) are considered,including the relationship with decision makers. If at least five of thefactors (1)-(6) have been assessed as “Highs”, of if at least four ofthe factors (1)-(6) have been assessed as “Highs” at the same time thatnone have been assessed as “Low”, then a “High” will be assigned to thequestion “Will we get it?”. The following combinations for the sixfactors (order is not important) will result in a “Medium” beingassigned to the question “Will we get it?”: H-H-H-H-L-L; H-H-H-M-M-M;H-H-H-M-M-L; H-H-H-M-L-L; H-M-M-M-M-M; H-H-M-M-M-L; H-H-M-M-L-L;H-M-M-M-M-M and H-M-M-M-M-L. The following combinations for the sixfactors (order is not important) during the prove and close phases willresult in a “Low” being assigned to the question “Will we get it?”:H-H-L-L-L-L; H-H-M-L-L-L; H-M-M-M-L-L; H-M-L-L-L-L; H-L-L-L-L-L;L-L-L-L-L-L; M-M-M-M-M-M; L-M-M-M-M-M; L-L-M-M-M-M; L-L-L-M-M-M;L-L-L-L-M-M and L-L-L-L-L-M. Again, in the prove and close phases, somecombinations can be treated as special cases with furtherconsiderations. For example, the combination H-H-H-H-M-L could result ina “High” assignment for the question “Will We Get It?” if both theinformation monitor and interaction monitor factors had been assessed as“High”, or could result in a “Medium” assignment if only one of theinformation monitor and interaction monitor factors had been assessed as“High”. The combination H-H-H-L-L-L could result in a “Medium”assignment for the question “Will We Get It?” if both the informationmonitor and interaction monitor factors had been assessed as “High”, orcould result in a “Low” assignment if only one of the informationmonitor and interaction monitor factors had been assessed as “High”.

With reference to FIG. 18, in the illustrated example, the computerassigned values to the questions “Will we get it?” and “Will it happen?”as determined based on the above described methodology are shown on theprobability matrix 2202 as a block 242. It will be recalled that eachblock or cube in the probability matrix represents a probability indexvalue from 1 to 9. As mentioned above, the message 2206 is derived froma comparison of the probability for a sale opportunity as determined bythe sales persons answers to the questions “Will it happen?” and “Willwe get it?” with the probability as determined by the sales automationsystem. Preferably, the content of message 2206 depends also on thelocation in sales cycle at the time that the message is presented. FIG.20 a provides a diagrammatic representation of example content for themessage 2206 in one possible case (Case 1). In the illustrated case,three different combinations of the sales representative's answers tothe questions “Will it Happen?” and “Will We get It?” are represented inthe matrix 270 (which corresponds to the probability matrix 210 of FIG.6 a) by the boxes labeled “User”, and three different combinations ofthe sales automation assigned values to these two questions arerepresented in the matrix 270 by the boxes labeled “Computer”. Asillustrated in FIG. 6 a each of the boxes in the matrix is associatedwith a probability index, and accordingly, Case 1 as illustrated in FIG.20 a corresponds to situations where the sales representative's answersto the questions “Will it happen?” and “Will we get it?” correspond toprobability indexes of 7, 8, or 9 (the “Gut Feeling Index” 272 in FIG.20 a), and the computer generated answers correspond to probabilityindexes of 1, 2 or 3 (the “Computer Index” 273 in FIG. 20 a). In Case 1,the content of the message 2206 will selected by the sales automationsystem 10 to includes a Header Message 274 of “Possible Strategies:”,followed by the text shown in Probe column 276 in the event that thesales cycle is in the probe stage, the text shown in Prove column 278 ifthe sales cycle is in the prove stage, or the text shown in the Closecolumn 280 is the sales cycle is in the close stage.

The various combinations and messages shown in Case 1 of FIG. 20 a areexamples of possible computer generated messages when a gap between thesales representative's “Gut Feeling Index” and the computer generatedprobability index differ. Other messages and combinations are possible,and in this regard, FIGS. 20 b-20 i illustrate, in the same format asFIG. 20 a, further Cases 2-9 illustrating possible content for themessage 2206 for various “Gut Feeling” and computer generatedprobability index combinations.

As mentioned above in respect of FIG. 18, further messages can begenerated in field 2208. Such messages take into further considerationthe interaction information and sales environment information that hasbeen provided by the sales representative for a sales opportunity (suchinformation being collected through the use of the interfaces shown inFIGS. 14 and 17 a-c and used by the interaction monitor 160 andinformation monitor 150).

For example, with respect to entered interaction information (i.e.information corresponding to the fields shown in FIG. 14), theinteraction monitor 160 is configured in one embodiment to causedifferent messages to be displayed in message area 2208 based on whatpercentage of interactions have occurred versus those that should haveoccurred, at a specific time in the sales cycle—as determined by thesales model. If not enough interactions have been completed, the summarymessage could be: “More interaction with the customer is needed”. Insome embodiments, the user may be able to request further information byclicking an appropriate button, In which case a list of interactionswhich should be completed will appear, including for example, one ormore of the following possible statements: “Make a phone call”; “Arrangea meeting”; “Do a demonstration”; “Submit a proposal”; and/or “Perform a______ (custom activity type)”. If the interaction monitor 160 concludesthat the user has completed all required interactions needed by thesales model then the message “All model interactions have beencompleted.” could be displayed in the dialog box message area 2208.

With respect to entered sales environment information, the informationmonitor 15 can be configured to cause various messages to be displayedin the dialog box messages area 2208 depending on the informationentered by the user in response to the probing skill related questionsof FIG. 17 a, the proving skill related questions of FIG. 17 b and theclosing skill related questions of FIG. 17 c. In a preferred embodiment,the messages are selected based on the location in the sales cycle atthe time that the message is provided.

By way of example, in one embodiment, if the user enters “Unknown” inrespect of some of the probing skill questions shown in FIG. 17 a andthe information monitor factor (i.e. the contribution of the informationmonitor to the question “Will we Get it” as discussed above) has beenassigned less than a high value, the information monitor 150 could causethe message “Probe more on these issues:” to be displayed, to which theuser can request further information by clicking an appropriate button,in which case a list identifying steps to take to get more informationin respect of the questions shown in FIG. 17 a will appear. In oneembodiment, the possible statements are “Fully ascertain the customer'srequirements”; “You must determine the customer's level of need”;“Evaluate the match between your solution and the customer'srequirement”; “Can the customer afford your solution?”; “Learn moreabout the customer's organization”; “How competitive is thissituation?”; “You must identify your competition”; “Identify theeconomic decision maker”; “Identify the technical decision maker”;“Identify the user decision maker”; “How much influence does theeconomic decision maker have?”; “How much influence does the technicaldecision maker have?”; “How much influence does the user decision makerhave?”; “What is most important to the economic decision maker?”; “Whatis most important to the technical decision maker?”; and/or “What ismost important to the user decision maker?”. It will be appreciated thatthese questions correspond to the probing skill questions shown in FIG.17 a.

If the information monitor determines that the user has performed enoughprobing at the relevant point in the sales cycle as defined by the salesmodel the message displayed can be: “Enough probing has been done up tothis point in the sales cycle”.

With respect to answers to the proving skill related questions as shownin FIG. 17 c, the user entered answers to the “degree of proof” questionis compared against the sales model to determine a message for displayin dialog message area 2208. In one embodiment, the sales model assumesthat 100% of proving must be achieved by the end of the prove phase andthat the degree of proof follows a linear relation to progress throughthe prove phase, i.e., at fifty percent through the prove phase, thesales representative must have fifty percent of the proving complete. Ifthe sales representative has proven less than he should at a particularpoint in the sales cycle, the message that appears is as follows:“Concentrate more attention on the Economic, Technical, and Userdecision maker(s).” If the user has done sufficient proving at therelevant point in the sales cycle, as defined by the model, then themessage is: “Enough proving has been done up to this point in the salescycle”.

With respect to closing skill questions of FIG. 17 c, in one embodimentthe Information Monitor 150 is configured to assume that one trial closeshould have been made by the end of the prove phase. It also assumesthat at least two trial closings should be made in the close phase. Ifthis does not occur, then the message shown is: “Attempt a trial close.”

Turning again to the probing skill questions of FIG. 17 a, the effect of“Low” grade answers to such questions will be considered (“Low” grademeaning answers having a negative connotation). Although the questionsin FIG. 17 a may have been answered—which contributes towardsinformation gathered—, in some cases the answers to those questionsmight adversely affect the issue of “Will we get it?” In this case, anappropriate message is shown in message area 2208. For example if thecompetitive pressure question have been answered as High or Med, themessage is: “Watch out for the competition!”

If the answer to the question regarding the “match of your product tothe customer's need” is Low or Med, the message is:” Configure yoursolution better with the customer's requirement.” If the match of yourprice to the customer's budget is Low or Med, the message is: “Overcomethe customer's objections to your price.”. In response to “High grade”answers to questions regarding probing skills, the information monitormay be configured to cause messages such as “The competitive pressure islow.”; if there is only moderate competitive pressure.”; “There is agood match between your product and the customer's needs.”; and or “Youare within the customer's budget.” to be displayed as appropriate.

Turning again to the proving skill questions of FIG. 17 b, in the eventthat the sales representative has indicated a poor relationship with thedecision makers, during the prove phase the information monitor 150 maycause the message “Improve your relations with the Economic, Technicaland User decision maker(s).” to be displayed in message area 2208. Inthe event that a good relationship or OK relationship has been indicatedby the user's answers, the respective messages: “Good relationship withthe Economic, Technical and User decision maker”; or “Satisfactoryrelationship with the Economic, Technical and User decision maker” maybe displayed.

It will be appreciated that the “intelligent messages” described aboveare merely exemplary, and different messages could be displayed andvarious different combinations of user inputted answers could beassociated with messages different than those set out above. In someembodiments, algorithms could be used to assign weighting values to someor all of the possible answers for the questions shown in FIGS. 17 a-c,and a cumulative value based on the user's answers determined, thecommutative value being used by the automation system in combinationwith none, some or all of the known information about the sales model,current sales cycle phase, and user and computer generated probabilityindexes to select appropriate predetermined advice messages to provideto the user.

It will thus be appreciated that various aspects of the presentinvention are directed towards providing real and tangible results. Forexample, aspects of preferred embodiments are directed towards animproved method of determining the probability that the salesopportunity will be won by the sales representative in order to assistin achieving consistency from salesperson to salesperson and improvingthe accuracy of forecasting, thereby minimizing the potential risk tothe company. The described method breaks from the traditional methods ofrequesting or assigning numerical percentages directly, and instead asksthe sales representative to answer, using one of three possible answersof “High”, “Medium” or “Low” two simple questions—“Will this salehappen”, and if it will, “Will we get it?”, from which a probabilityindex is determined based on the nine possible answers.

According to a further aspect, the invention is directed towards a newmethod of determining what priority should be assigned to a salesopportunity, to indicate the degree of effort that a salesperson shouldgive to one opportunity over another. The described method derivespriority from assessing probability in the context of point of time inthe sales cycle. Particularly, the method removes the effect ofdifferent sales cycle lengths by normalizing the sales cycle to threetime phases during which a fundamental selling skill is predominant.This procedure has the effect of normalizing sales cycles of differentlengths. This procedure is also beneficial because it reinforces use ofthe fundamental sales skills as the salesperson reviews their list ofopportunities.

Another aspect of the present invention is directed towards having acomputer determine its own probability and priority through analysis ofanswers to questions presented to the sales representative throughoutthe sales cycle. In a preferred embodiment, intelligent responsetechnology is used to enable the computer to provide messages of adviceas to how to strategize to win the sale, regardless of whether thecomputed probability is the same or different from the probabilitycalculated using the salesperson's gut feel answers to the two abovequestions. The probability and priority values are used together withfeatures of the sales model to construct reports advising where therepresentative should best spend his time. These reports can, forinstance, use the information stored in the sales model to assesswhether sufficient customer interactions have occurred, or whethersufficient information has been gained at a particular point in thesales cycle.

It will be apparent to persons skilled in the art that many variationsto the embodiments described above are contemplated. For example, it ispossible to implement a certain portion of the invention in apaper-based system, though it is unlikely that this would be the methodof choice, considering technology is a comparatively inexpensive optionfor the average business.

The forgoing description is clearly by way of example only and is notmeant to limit the scope of protection to be accorded to the invention,which scope is defined by the following claims.

1. A computer implemented method of assisting a sales representativewith prioritizing a plurality of sales opportunities, comprising thesteps of: for each of the sales opportunities, determining a probabilityfrom a finite number of possible probability values that the salesrepresentative will ultimately win the sale; establishing a sales cycletime line for each of the sales opportunities and dividing each of therespective sales cycle time lines into a uniform number and type ofselling phases; for each sales opportunity, assigning a time-dependentpriority value based on the determined probability values for the salesopportunity and the selling phase that the sales opportunity is in atthe time that the priority value is assigned; and providing on a visualdisplay for the sale representative an indication of the time-dependentpriority value assigned to at least one of the sales opportunities. 2.The method of claim 1 wherein each of the sales opportunities aredivided into a probing phase during which probing type skills dominatethe sales opportunity, a proving phase during which proving type skillsdominate the sales opportunity, and a closing phase during which closingskills dominate the sales opportunity.
 3. The method of claim 1including establishing relative priority indices for each uniquecombination of possible probability values and possible selling phase,wherein said priority values are based on the relative priority indices.